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Open Access

ARTICLE

MCCGAA: Multimodal Channel Compression Graph Attention Alignment Network for ECG Zero-Shot Classification

Qiuxiao Mou, Haoyu Gui, Xianghong Tang*, Jianguang Lu
State Key Laboratory of Public Big Data, Guizhou University, Guiyang, China
* Corresponding Author: Xianghong Tang. Email: email
(This article belongs to the Special Issue: Advances in Time Series Analysis, Modelling and Forecasting)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.076251

Received 17 November 2025; Accepted 13 January 2026; Published online 13 February 2026

Abstract

Electrocardiogram (ECG) is a widely used non-invasive tool for diagnosing cardiovascular diseases. ECG zero-shot classification involves pre-training a model on a large dataset to classify unknown disease categories. However, existing ECG feature extraction networks often neglect key lead signals and spatial topology dependencies during cross-modal alignment. To address these issues, we propose a multimodal channel compression graph attention alignment network (MCCGAA). MCCGAA incorporates a channel attention module (CAM) to effectively integrate key lead features and a graph attention-based alignment network to capture spatial dependencies, enhancing cross-modal alignment. Additionally, MCCGAA employs a log-sum-exp loss function, improving classification performance and convergence over the original clip-style method. Experimental results show that MCCGAA outperforms current methods, achieving the highest classification accuracy across six publicly available datasets. MCCGAA holds promise for advancing ECG zero-shot classification and offering better decision support for researchers.

Keywords

ECG zero-shot classification; contrastive learning; cross-modal alignment; graph attention network; channel attention mechanism

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